The Fiscal Multiplier and Economic Policy Analysis in the United States

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I. INTRODUCTION

The Great Recession, which began in December 2007 and ended in June 2009, sparked wide interest in the economic effects of fiscal policy. The downturn initially provoked a flurry of papers estimating how stimulus packages such as the American Recovery and Reinvestment Act of 2009 (ARRA) would affect output and employment. (1) Later, as many policymakers in the United States and Europe sought to reduce government deficits, much attention shifted to the likely effects of fiscal consolidation (increases in taxes and/or decreases in government spending or transfers). (2) In addition, the entire period of recession and slow recovery has seen the release of numerous studies examining how changes in fiscal policy affect economic outcomes. (3)

The recent interest in how fiscal policy affects the economy is reflected in an ongoing debate over the size of the fiscal multiplier, the change in a nation's economic output generated by each dollar of the budgetary cost of a change in fiscal policy. The multiplier must be estimated; it cannot be observed. (4) Estimates of the fiscal multiplier vary widely, including values in excess of one and less than zero. (5)

What models do economists use to estimate the multiplier? Why do estimates of it vary widely? And how can economists use those estimates to judiciously analyze U.S. economic policy? We address the first two questions by reviewing the rapidly expanding body of academic literature and address the third question by providing an overview of how the Congressional Budget Office (CBO) uses multiplier estimates to analyze fiscal policy proposals and legislation.

II. WHAT MODELS DO ECONOMISTS USE TO ESTIMATE THE FISCAL MULTIPLIER?

Three types of models are often used to generate estimates of the fiscal multiplier--macroeconometric forecasting models, time series models, and dynamic stochastic general equilibrium (DSGE) models. Each type has strengths and limitations.

A. Macroeconometric Forecasting Models

Macroeconometric forecasting models, which underlie most of the forecasts offered to the clients of economic consulting firms, are the basis for many estimates of multipliers. The details of those models are based largely on historical relationships between aggregate economic variables and informed by theories of how those variables are determined. Because macroeconometric forecasting models emphasize the influence of the overall demand for goods and services, they tend to estimate greater economic effects from policies that bolster demand than time series models and DSGE models do. (6)

The reliability of macroeconometric projections depends heavily on the validity of the specific economic assumptions used. For example, because the models are grounded in observed historical relationships, their estimates rely on the assumption that individuals will, on average, continue to react to the changes in fiscal policies in the same way that they reacted in the past. Consequently, estimates projected by such models might be unreliable when policies or economic conditions differ substantially from those of the past. (7)

B. Times Series Models

Time series models offer an alternative to macroeconometric forecasting models. In their most basic form, time series models, such as vector autoregression (VAR) models, summarize correlations between economic variables--such as government spending and gross domestic product (GDP)--over time. (8) Because time series models are grounded in historical data and contain little economic theory, they can be useful particularly when there is a reason to believe that existing theories may be inaccurate or based on particularly unrealistic assumptions.

However, the lack of theoretical grounding makes it difficult to use time series models to assess the direction of causation between policies and the economy. This is known as the "identification" problem. …